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Record W2803303832 · doi:10.1364/josaa.35.000977

Precision of polarimetric orthogonal state contrast estimation in coherent images corrupted by speckle, Poisson, and additive noise

2018· article· en· W2803303832 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of the Optical Society of America A · 2018
Typearticle
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsnot available
FundersDirection Générale de l’ArmementOntario Ministry of Research, Innovation and ScienceAgence Nationale de la Recherche
KeywordsCramér–Rao boundPolarimetrySpeckle patternContrast (vision)Speckle noiseMonte Carlo methodUpper and lower boundsPoisson distributionAlgorithmOpticsComputer scienceEstimation theoryPhysicsMathematicsStatisticsScatteringMathematical analysis

Abstract

fetched live from OpenAlex

We consider laser-illuminated active polarimetric imaging systems that measure the orthogonal state contrast (OSC), a frequently used surrogate to the degree of polarization, that can be used, for example, to discriminate manmade objects from natural backgrounds in remote sensing. We investigate the estimation precision of the OSC parameter in the presence of speckle, Poisson, and additive noise by using the Cramer-Rao lower bound (CRLB). Using Monte Carlo simulations and optical experiments, we show that the expression of the CRLB models the actual OSC estimation performance with excellent accuracy. This result is important for the design and sizing of active polarimetric imagers since the closed-form expression of the CRLB makes it handy for back-of-the-envelope calculations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.227
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it